Fairvisor for LLM Providers

What Fairvisor Does for LLM Providers

Multi-Dimensional Quotas

Control more than requests per second:

Tokens/minute, tokens/day, and cost/minute
Limits by endpoint type, model, and route
Burst + sliding-window controls and sustained throughput caps for long-running campaigns

Identity-Aware Enforcement

Policy follows identity, not just IP:

API key + tenant + user + IP/ASN/geo + UA bot signals
Distinct trust profiles per key or customer segment

Repeated-Request Detection

Fairvisor detects automated loops: identical requests from the same identity within configurable time windows. If the same request (matched by content fingerprint) repeats above a threshold, Fairvisor can reject, throttle with progressive delay, or warn. Configurable threshold and window per key or tenant. → Loop Detector

Edge Enforcement Playbooks

Automate responses in real time:

Throttle (progressive delay)
Hard block (429/503)
Shadow mode dry-run before enforcement

Forensics and Auditability

Produce proof for security and legal workflows:

Who initiated traffic, when, and through which identity path
Token/cost impact over time
Which controls fired and why
Exportable incident timeline for compliance and IR

Cost-Based Enforcement

Stop overages before they happen:

Per-key and per-tenant budget counters enforced on every request
Configurable cost limits per model, endpoint, and customer segment
Enforcement triggers before overage — not after the bill arrives

What Repeated-Request Loops Look Like

Automated abuse doesn’t always look like DDoS. The clearest signal Fairvisor catches is exact repetition.

Key rotation loop — When one key hits its limit, traffic shifts to a new key within seconds. If the request content fingerprint is identical, Fairvisor’s loop detector fires on the new key as well. → LLM Token Limiter | Loop Detector

Automated retry storm — A client retries the same failed request hundreds of times within a short window. Loop detection identifies the pattern and triggers throttling or rejection rather than letting retries exhaust token budgets.

Minimal MVP Scope (Practical Rollout)

Start with three controls:

Token/cost limits with burst controls
Repeated-request (loop) detection with configurable threshold and window
Automated responses (throttle, block) with complete audit log

This is enough to make a credible claim: reduced risk and attack economics for model extraction attempts, with measurable controls.

Who This Is For

Teams exposing LLM functionality as paid API products
Vertical copilots with expensive prompt pipelines and proprietary system prompts
Platforms where behavioral cloning risk is material

FAQ

What types of rate limits can I apply per model?

Token/minute, token/day, cost/minute — configurable per model, endpoint type, or customer segment. Separate burst and sustained throughput controls for limiting both spike and long-running campaign traffic.

How does repeated-request detection work?

Fairvisor fingerprints each request (CRC32 of the content) and tracks how many times the same fingerprint appears from the same identity within a configurable time window. When the count exceeds the threshold, the configured action fires: reject, throttle (progressive delay), or warn. → Loop Detector

How does Fairvisor track identity across key rotation?

Each key’s requests are fingerprinted independently. If a new key immediately begins sending the same exact requests that triggered loop detection on a previous key, loop detection applies to the new key as well once its own threshold is exceeded. → Loop Detector docs

Does Fairvisor support streaming (SSE) responses?

Yes. Token counting happens during streaming. If a completion exceeds configured limits mid-stream, Fairvisor closes the stream gracefully with finish_reason: length. No corrupted responses, no wasted tokens after the cutoff.

Can we run controls in shadow mode before hard enforcement?

Yes. Start in shadow mode to observe loop detection and quota signals against real traffic, tune thresholds, and validate enforcement behavior before enabling throttle/block actions.

How do I produce evidence for compliance or incident response?

Fairvisor logs every enforcement action with the full identity path (key, tenant, user), the rule that fired, and the token/cost impact. Logs are exportable as structured incident timelines — usable directly in security reviews, IR workflows, and compliance audits.

Why teams choose Fairvisor

Control, detect, and prove

Multi-dimensional quotas, repeated-request detection, and auditable evidence — all enforced at the edge.

Abuse firewall at the inference layer

Token and cost limits combined with loop detection that catches automated collection patterns before they exhaust your budget.

Anti-extraction by design

Make model distillation economically infeasible before prompt harvesting turns into a behavioral clone.

Shadow mode before hard enforcement

Observe quota signals and loop detection against real traffic, tune thresholds, and validate enforcement behavior before enabling throttle or block actions — zero risk to production.

Forensics for security and legal

Exportable incident timelines with identity path, token impact, and control audit trail — proof that holds up in compliance reviews and IR workflows.

OpenAI-compatible enforcement

Works with existing SDK clients. Token budget exhaustion produces standard error formats so retry paths continue to function without application rewrites.

Put anti-extraction controls in front of your model endpoints

Deploy in shadow mode

Also relevant

For AI Teams

Token budgets, loop detection, and cost controls for LLM agents in production.

For FinOps

Real-time cost attribution and budget enforcement by tenant, team, and endpoint.

For Compliance

Immutable audit logs, RBAC, and SOC 2 control mapping.

Make model extraction economically infeasible